Image Classification using Convolution Neural Network Based Hash Encoding and ... Indeed, in the past few years, Deep Learning (DL) has been considered as a backbone for image analysis using ...

Crop classification using polarimetric SAR data is one of the most important applications in Polarimetric Synthetic Aperture Radar (PolSAR) imagery. Obviously, for crop classification, multi-temporal ...

Spiking neural networks (SNNs), on the other hand, use different methods of encoding and processing data, making their behavior more similar to brain activity. This motivated us to explore SNNs in ...

In this article, we will implement the multiclass image classification using the VGG-19 Deep Convolutional Network used as a Transfer Learning framework where the VGGNet comes pre-trained on the ...

Since this project is going to use CNN for the classification tasks, the row vector, (3072), is not an appropriate form of image data to feed. In order to feed an image data into a CNN model, the ...

An example of an image classification problem is to identify a photograph of an animal as a "dog" or "cat" or "monkey." The two most common approaches for image classification are to use a standard ...

The Data Science Lab. CIFAR-10 Image Classification Using PyTorch. CIFAR-10 problems analyze crude 32 x 32 color images to predict which of 10 classes the image is. Here, Dr. James McCaffrey of ...